Theoretical and Computational Chemistry

Machine Learning for Materials Scientists: An Introductory Guide Towards Best Practices



This Editorial is intended for materials scientists interested in performing machine learning-centered research.

We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking datasets, model and architecture sharing, and finally publication.
In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed.

Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.


Thumbnail image of BestPractices_submitted.pdf

Supplementary material

Thumbnail image of BestPractices paper - SI.pdf
BestPractices paper - SI